Surgical assistance planning method using lung motion analysis

ABSTRACT

A medical analysis method for estimating a motion vector field of the magnitude and direction of local motion of lung tissue of a subject is described. In one embodiment a first 3D image data set of the lung and a second 3D image data set is obtained. The first and second 3D image data sets correspond to images obtained during inspiration and expiration respectively. A rigid registration is performed to align the 3D image data sets with one another. A deformable registration is performed to match the 3D image data sets with one another. A motion vector field of the magnitude and direction of local motion of lung tissue is estimated based on the deforming step. The motion vector field may be computed prior to treatment to assist with planning a treatment as well as subsequent to a treatment to gauge efficacy of a treatment. Results may be displayed to highlight.

CROSS-REFERENCE TO RELATED APPLICATIONS

None

FIELD OF THE INVENTION

The present invention relates to surgical procedures and in particular,to assisting physicians with planning surgical procedures in the lung.

BACKGROUND OF THE INVENTION

The use of computer generated 3D models of a patient's anatomy is wellestablished. Such 3D models are based on real image data taken from thepatient. The 3D models assist surgeons in planning a surgical procedure.

A number of commercially available systems process 3D image data sets ofa patient and prepare a 3D model of the organ for the physician toreview in advance of surgery. In some systems, virtual markers andregions of interests may be added or superimposed on the 3D model forreview and adjustment by the physician. An example of such a planningsystem with application to the lung is the LungPoint® Planning Systemmanufactured by Broncus Technologies, Inc. (Mountain View, Calif.).

A challenge with certain planning systems is to compensate for rhythmicor tidal motion of the organ arising from, e.g. breathing. An implant oranatomical feature positioned in the lung based on a 3D image of thelung in one state (e.g., full inspiration) may have a different positionwhen the lung is in another state (e.g., full expiration). Failure tocompensate for the displacement of the anatomy, implant, or device asthe case may be could thwart the procedure and perhaps in some instancesresult in patient injury should the implant or device damage a vessel,pleural surface, or other sensitive region.

A number of techniques address to varying degrees of effectiveness theabove mentioned challenge. For example, the paper entitled “FastDeformable Registration on the GPU: A CUDA Implementation of Demons” byPinar Muyan-Ozcelik, 2008 (hereinafter “the Muyan-Ozcelik paper”)describes a deformable registration technique to map CT scans takenduring expiration to CT scans taken during inspiration. However, amongstother things, the Muyan-Ozcelik paper does not appear to compensate forposture and position of the patient prior to deforming the CT images.Accordingly, more is required in order to accurately estimate a motionvector field of the magnitude and direction of local motion of anon-rigid organ such as the lung.

A method and system to assist surgeons to plan medical procedures with awide variety of medical implements in a body organ, that has applicationto non-rigid organs such as the lung, and that does not suffer the aboveidentified drawbacks is therefore desired.

SUMMARY OF THE INVENTION

One embodiment of the present invention is a method for assisting asurgeon to analyze the motion of a lung. Two 3D CT images are obtainedas input. One 3D CT image represents the lung during a first state suchas inspiration and a second 3D CT image represents the lung during asecond state such as expiration. A rigid registration is performedbetween the two 3D CT images to align the images. The rigid registrationis followed by a deformation until the two 3D CT images match. A motionvector field corresponding to the local motion of the lung to move fromthe first state to the second state is generated and output of themethod.

In another embodiment of the present invention the motion vector fieldis displayed such that areas of greatest motion have the highestintensity.

In another embodiment of the present invention, efficacy of surgicaltreatments, or displacement of medical implements (such as devices,implants, and or fiducials) may be computed and displayed based on areview of the motion vector field.

In another embodiment of the present invention, a second vector motionfield is estimated following a treatment. The second vector motion fieldis compared to the first vector motion field to identify regions ofgreatest change or to illustrate change in local motion of the lungtissue or the lack there of.

In another embodiment of the present invention an atlas motion vectorfield is created and stored. The atlas motion vector field is applied toa first 3D image data set of a patient such that the local motion of thelung tissue may be estimated based on the atlas motion vector field andthe first 3D image data set.

In another embodiment of the present invention a system comprises aprocessor operable to estimate a motion vector field based on receivedimage data as described herein.

In another embodiment, the system further includes a display which showsan animation of the motion of the lung and the displacement of a medicalimplement in the lung.

The description, objects and advantages of the present invention willbecome apparent from the detailed description to follow, together withthe accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of a workstation for performing a lungmotion analysis for assisting a physician to plan a medical procedure.

FIG. 2 is a block diagram of a lung analysis system in accordance withone embodiment of the present invention.

FIG. 3 is a flow chart of a method to visualize motion of a medicalimplement in a lung.

FIG. 4 is a flow chart of a method to observe the functionality ofregion in a lung.

FIGS. 5-6 are same slices of CT images of a lung of a patientcorresponding to inspiration and expiration respectively.

FIG. 7 is a CT image after performing a rigid alignment on the 3D imagedata of FIG. 5 to the slice shown in FIG. 6 such that the main carina isnow aligned in both the images at the same slice.

FIG. 8 is a CT image after performing a deformable registration on theCT image from FIG. 7 to register with the CT image of FIG. 6 such thatthe local lung structures align.

FIG. 9 is an illustration of a motion vector field of the lungcorresponding to the slice shown in FIG. 6 wherein brighter areascorrespond to larger breathing motion.

DETAILED DESCRIPTION OF THE INVENTION

Before the present invention is described in detail, it is to beunderstood that this invention is not limited to particular variationsset forth herein as various changes or modifications may be made to theinvention described and equivalents may be substituted without departingfrom the spirit and scope of the invention. As will be apparent to thoseof skill in the art upon reading this disclosure, each of the individualembodiments described and illustrated herein has discrete components andfeatures which may be readily separated from or combined with thefeatures of any of the other several embodiments without departing fromthe scope or spirit of the present invention. In addition, manymodifications may be made to adapt a particular situation, material,composition of matter, process, process act(s) or step(s) to theobjective(s), spirit or scope of the present invention. All suchmodifications are intended to be within the scope of the claims madeherein.

Methods recited herein may be carried out in any order of the recitedevents which is logically possible, as well as the recited order ofevents. Furthermore, where a range of values is provided, it isunderstood that every intervening value, between the upper and lowerlimit of that range and any other stated or intervening value in thatstated range is encompassed within the invention. Also, it iscontemplated that any optional feature of the inventive variationsdescribed may be set forth and claimed independently, or in combinationwith any one or more of the features described herein.

All existing subject matter mentioned herein (e.g., publications,patents, patent applications and hardware) is incorporated by referenceherein in its entirety except insofar as the subject matter may conflictwith that of the present invention (in which case what is present hereinshall prevail).

Reference to a singular item, includes the possibility that there areplural of the same items present. More specifically, as used herein andin the appended claims, the singular forms “a,” “an,” “said” and “the”include plural referents unless the context clearly dictates otherwise.It is further noted that the claims may be drafted to exclude anyoptional element. As such, this statement is intended to serve asantecedent basis for use of such exclusive terminology as “solely,”“only” and the like in connection with the recitation of claim elements,or use of a “negative” limitation. It is to be appreciated that unlessdefined otherwise, all technical and scientific terms used herein havethe same meaning as commonly understood by one of ordinary skill in theart to which this invention belongs.

FIG. 1 is an illustration of a system in accordance with one embodimentof the present invention. In particular, FIG. 1 illustrates aworkstation 50 in communication with a first set of image data 52 and asecond set of image data 54. The first set of image data and second setof data may correspond to, for example, 3D CT image data of the lung ofa patient during inspiration and expiration respectively.

As will be described herein, the image data is received by theworkstation and processed to compute or estimate a motion vector fieldof the lung which indicates local motion of the lung. The motion vectorfield may then be presented in various formats to the user on a display60. As will be discussed further herein the user may insert a virtualmedical implement 30 using a keyboard 82 and/or a mouse 84. Theboundary, volume, and motion of the medical implement 30 may be computedbased on the local motion vector field and displayed on monitor 60. Thedisplay may show the movement of the virtual medical implement to assista physician to plan a surgical procedure.

Also, by “medical implement”, it is meant to include but not be limitedto implants and devices which may be used for treatment or diagnosis,and may be located in the subject permanently, or temporarily as thecase may be. Examples of medical implements, without limitation, includeimplants, catheters, needles, ablation devices, stents, valves, fiducialmarkers, seeds, coils, etc.

FIG. 2 illustrates a system 90 including a workstation or programmedcomputer 50. The workstation 50 shown in FIG. 2 includes a processor 70operable to estimate the motion vector field of the lung based on,amongst other things, the multiple sets of 3D image data as will bedescribed in more detail herein.

Workstation 50 is shown having a memory device 80 which holds or storesinformation including imaging, device, marker, and procedural data. Thememory device may be a hard drive, for example. It is to be understood,however, that although the system in FIG. 2 is shown with a memory 80for receiving and storing various information the invention is not solimited. In an alternative embodiment the system may be configured tomerely access a memory device such as a USB stick, a CD, or other mediastorage device.

In another embodiment the processor is connectable to a memory devicethrough the internet or through another communication line to access anetwork. For example, patient data CT scans may be stored on a server ofa hospital and the processor of the present invention is adapted toaccess such data via a communication line and process the data. Theworkstation may communicate with the DICOM, for example, to receive suchdata sets.

The workstation 50 is adapted to send image data to a display 84 using avideo card 82. An example of a workstation is a Dell Computer Model No.T5400, with Dual-core Intel Xeon 2.0 GHz processor, and a Nvidia QuadroFX 3800 video card. A frame grabber card 72 may optionally be providedto capture live video or image data as desired.

As mentioned above, the system 90 shown in FIG. 2 also includes adisplay 84 which may present reports, data, images, results and modelsin various formats including without limitation graphical, tabular,animated, and pictorial form. Workstation 50 is configured to send tothe display a number of types of images including 3D model views, 2Dmodel fluoroscopy views, real fluoroscopy views, real endoscopic views,model endoscopic views, and a wide range of information superimposed onthe views such as without limitation planning information, regions ofinterest, virtual target markers, vessels, virtual obstacles, realdevices, virtual devices, routes to a target, notes and indicia providedby the user, etc. In one embodiment of the present invention, a medicalimplement is superimposed on a 3D model of the organ. In the case thatplanning information is to be utilized and or displayed, planninginformation may be provided or determined by the workstation asdescribed in U.S. Patent Application No. 2008/0183073 to Higgins et al.

The system 90 shown in FIG. 2 also includes a user input device 86 suchas, for example, a keyboard, joystick, or mouse. The user input deviceallows a user such as the physician to add or input data and informationas well as modify planning information and to make notes in the filesand records.

Displays may be incorporated with the processor in an integrated system(e.g., a lap top, tablet computer, or touch screen pad-type computer) orthe displays may cooperate with the processor from a remote location. Aprocessor may be adapted to send or deliver data across a network to oneor more displays or portable computer devices or smart phones such asthe iPhone® manufactured by Apple, Inc. Cupertino, Calif., UnitedStates. Indeed, although the computer system 90 shown in FIG. 2 includesa number of various components incorporated into a system, the inventionis not so limited. The invention is intended to be limited only asdefined in the appended claims.

FIG. 3 is a flow chart illustrating an overview of a procedure 100 forassisting a physician plan a medical procedure using a medicalimplement. The steps shown in FIG. 3 may be carried out on a programmedcomputer or system and include: step 110 receiving 3D image data of thelung in a first state; step 112 receiving 3D image data of the lung in asecond state; step 120 registering the 3D image data from the firststate with that of the second state using a reference or anatomicalfeature; step 130 deforming the 3D image from the first state to matchthat of the second state; step 140 estimating a vector field for thelocal motion of the lung representing the local motion of the lung fromstate one to state two; and step 150 computing displacement of a virtualmedical implement placed in the 3D image and subject to the motionvector field from the estimating step.

Steps 110 and 112 include receiving 3D image data of the lung in a firststate and a second state. In one embodiment, receiving 3D image data ofthe lungs of a subject includes receiving high resolution computedtomography (HRCT) scans. FIGS. 5-6 show exemplary 2D CT image slicescorresponding to inspiration and expiration of a patient, respectively.

Other acceptable data sets include without limitation MRI, 3Dangiographic, and X-ray data sets. The image data may vary widely andbe, for example, a 3D image file, 3D image volume, 3D image data set, ora set of 2D images of the organ from which a 3D model of the organ maybe computed. An exemplary technique to determine a 3D model of the bodyorgan is disclosed in U.S. Pat. No. 7,756,316 entitled “Method andsystem for automatic lung segmentation”. See also, U.S. Pat. Nos.7,889,905 and 7,756,563; and Patent Publication No. 2008/0183073 all toHiggins et al.

Also, it is to be understood that the “state” of the lung may varywidely. Non-limiting examples include: the lung at full inspiration,full expiration, partial inspiration or expiration, or anywhere inbetween full inspiration and full expiration; a state arising from acondition, treatment, diagnosis; a state arising from the environment,patient position, or a healthy state or control state. The second stateof the lung is a state of the lung other than that of the first state.Reference to “first” and “second” is arbitrary and is not intended tolimit the invention to a particular order or sequence except where suchorder arises from the context as a whole.

Step 120 recites registering of the 3D images with a feature such as ananatomical feature. An exemplary approach to carrying out step 120 is torigidly align two CT images with respect to each other so that there isone-to-one correspondence between some anatomical features. Non-limitingexamples of anatomical features include the spinal column or maincarina.

FIG. 7 shows the result after performing rigid alignment between the 3Dimage file corresponding to the FIG. 5 with that of FIG. 6. The imageshown in FIG. 7 is rigidly aligned with that of FIG. 6, namely, thespinal column is matched. Selection of appropriate anatomical featuresto which rigid matching is performed provides a step in determininglocal tissue movement, e.g., during the respiratory cycle. As standardCT scans are often taken in the supine position, the spine has minimaldeflection between different breathing states. Using the matched spinesprovides an anatomical reference system origin. Note, however, at thisstage the lung tissue is still unmatched with respect to the localmovement of tissue between the two states.

A number of approaches may rigidly register the 3D images. An approachis generating point correspondence. The point correspondence can begiven manually or generated automatically. For example, the TreeMatching tool (as described in Michael Graham and William E. Higgins.“Optimal Graph-Theoretic Approach to 3D Anatomical Tree Matching,” IEEEInternational Symposium on Biomedical Imaging (ISBI), pp. 109-112, April2006) can perform automatic correspondence of airway branches. Given thecorrespondence, a 3D alignment and re-sampling of the CT images placesthe images in the same global coordinate system. Another approach torigidly align the two images is based on automatically aligningstructures of interest. Certain structures of interest such as thespinal column are relatively fixed during the breathing cycle and may bealigned by a variety of methods. One such method is to automaticallysegment the structures in the images at both states, for example, usingthresholding, region growing, template matching, or parametricapproaches, described in U.S. Patent Publication No. 2008/0044074, filedAug. 9, 2007 to A. Jerebko. After such images are segmented, animage-matching gradient descent algorithm can be used to determine thedisplacement and rotation between the segmented organs such as theLucas-Kanade image-matching gradient descent algorithm described in B.Lucas and T. Kanade. An iterative image registration technique with anapplication to stereo vision. In Proceedings of the International JointConference on Artificial Intelligence, pages 674-679, 1981.

Step 130 recites deformable registration. A deformable registrationmoves each voxel in one of the images (e.g., source image) to match itsintensity value in the other image (e.g., target image). An approach toperform this step includes minimizing a cost function (such assum-of-squared-differences) around each voxel. Noise or structures (suchas fiducial markers) may be masked out which need not be aligned priorto this registration. A suitable algorithm to perform this step is thedemons algorithm, described in the Muyan-Ozcelik paper, referencedabove. However, other algorithms may be used for implementation of thedeformation step.

FIG. 8 shows a result after performing deformable registration betweenthe 2D image of FIG. 7 to that of FIG. 6. The image shown in FIG. 8 isdeformed such that all the voxels have been aligned. The upper edge 350of the image evidences deformation as well.

An output of the deformable registration step is illustrated by step 140of FIG. 3. In particular, in this embodiment an output is the estimationor generation of a motion vector field. The motion vector field providesa direction and magnitude of each point in the 3D image of the lungindicating the movement and location between the different states of thelung. An illustration of a vector motion field corresponding to the FIG.6 slice discussed above is shown in FIG. 9. The intensity isproportional to the breathing motion at each voxel. The brighter areascorrespond to larger breathing motion.

Step 150 recites computing the displacement of a medical implement. Thisstep is based on the motion vector field. In one embodiment, the medicalimplement is a fiducial, and fiducial displacement is calculated sometime after initial placement.

Though not illustrated as a step, the information computed and receivedfrom the above described steps may be displayed in a wide variety ofways. In one embodiment, a virtual medical implement is superimposedonto a 3D view of the body organ. The estimated motion can be applied toanimate any 2D or 3D view generated from one of the CT images.Additional markers, routes, indicia and planning information may bedisplayed with the images as well. The physician may then observe theestimated motion of the medical implement and the consequences of itsdisplacement prior to performing the surgery. The physician maymanipulate the virtual medical implement until determining a candidatelocation and or route.

FIG. 4 is a flow chart illustrating an overview of a procedure 200 toassist a physician in evaluating the efficacy of lung function or atreatment performed on the lungs.

First, step 210 recites estimating a first motion vector field. Themotion vector field may be estimated on a subject as described above inconnection with FIG. 3. An output from the step 210 is a first motionvector field of the lung indicating the local motion of the lung betweena first state and second state such as, for example, inspiration andexpiration.

Next, step 220 recites performing a treatment on the subject. Forexample, treatments may vary widely and include without limitationsurgical treatments and interventions such as RF ablation, lung volumereduction, the placement of airway valves or stents, as well as deliveryof medications, physical therapy, and conservative care treatments.

Step 230 recites estimating a second motion vector field. Step 230 isperformed subsequent to the treatment step. However, in anotherembodiment, step 220 may be omitted and the second motion vector fieldis estimated after a time period, wait, bed rest, or for example aperiodic or annual checkup. Step 230 is carried out the same as thatdescribed in step 210 except step 230 is performed following thetreatment. The output from step 230 is a second vector motion field.

Step 240 recites computing a difference between the first motion vectorfield and the second vector motion field. Areas of the greatestdifference may be highlighted similar to the image shown in FIG. 9.Alternatively, data may be automatically calculated by the processor tolist voxels having the largest change.

Areas of greatest change may correspond to a number of conditions andregions in the lung. For example, a large change in local motion maycorrespond to improvement in function as would be hoped or expected inan emphysema treatment to increase the inspiration and expiration. Orperhaps, areas of largest change could correspond to the regions of themost diseased tissue (e.g., where tissue is displaced do to rapidlygrowing tumor). Regardless of the type of disease or diagnosis orobservation, the method of the present invention provides the physicianadditional data to analyze the motion of the lung.

Step 250 recites displaying the results. Results and data may bedisplayed in a wide variety of formats and tailored to the desire of thephysician. In one embodiment, a display shows the first and secondmotion vector fields adjacent one another. Showing the first and secondmotion vector fields adjacent one another is one embodiment allowing thephysician to conveniently compare and contrast local lung motion priorto and after a treatment or time period. Improvement in motion beforetreatment and after treatment may be ascertained wherein regions oflargest change are indicated in intensity, arrow length, or otherindicia.

In another embodiment of the present invention, a medical analysismethod for estimating a motion vector field of the magnitude anddirection of local motion of lung tissue of a first subject includes a)receiving first image data of the lung of the first subject in a firststate; and b) estimating the local motion of the lung of the firstsubject based on an atlas motion vector field.

Generating the atlas motion vector field may be carried out by creatingor estimating multiple vector motion fields corresponding to multiplepatients. To generate the atlas, the motion vector field is determinedfor a plurality of data sets of different subjects. Because subjectsvary in size and shape, a regularization and registration of the motionmodels is required to align the different motion fields. This can bedone, for instance, by matching the spines, or lungs of differentpatients and applying an affine warp of the motion fields of differentsubjects such that the motion vector fields are all brought to aregularized coordinate system.

With this model, the atlas motion vector field is applied to the firstimage data of the first subject to predict the motion of the lung of thefirst subject. While the actual motion of the lung tissue between twostates in the patient may be unknown, it can be predicted from anensemble average to estimate local motion. In this manner, only one 3Dimage data set is required of the subject and, using the atlas motionvector field, the local motion of the lung of the first subject ispredicted.

Any of the above described method may be carried out on a system.Indeed, the invention in one embodiment is a workstation having aprocessor programmed to carry out any of the above described methods.

Other modifications and variations can be made to the disclosedembodiments without departing from the subject invention.

We claim:
 1. A medical method for observing the local motion of amedical implement in a lung of a subject comprising the steps of: a)receiving first image data that includes a lung tissue of the lung in afirst state; b) receiving second image data that includes the lungtissue of the lung in a second state different than the first state; c)rigidly registering the first image data and the second image data withone another using an anatomical reference wherein the anatomicalreference excludes the lung tissue and is a relatively fixed anatomicalstructure having minimal deflection between breathing states of thelung; d) based on the anatomical reference from the rigidly registeringstep, deforming at least one of the following: i) the first image dataof the lung to match the second image data of the lung, and ii) thesecond image data of the lung to match the first image data of the lungby moving voxels in at least one of the first image data or the secondimage data until voxel intensity values of the first and second imagedata are matched; and e) generating a motion vector field of themagnitude and direction of local motion of the lung tissue based on therigidly registering step and deforming step wherein the local motion ofthe lung tissue between first and second states is independent of theanatomical reference defined in the rigidly registering step.
 2. Themethod of claim 1 further comprising receiving a 3D location of acandidate medical implement.
 3. The method of claim 2 further comprisingcomputing displacement of the candidate medical implement based on themotion vector field.
 4. The method of claim 3 further comprisingdisplaying the displacement of the medical implement.
 5. The method ofclaim 4 wherein said medical implement is selected from the groupconsisting of a medical implant and a medical device.
 6. The method ofclaim 5 wherein the medical implement is a fiducial marker.
 7. Themethod of claim 1 wherein the anatomical reference is a spinal column.8. The method of claim 1 wherein the first state corresponds to the lungduring an inspiration.
 9. The method of claim 3 further comprisingplacing a selected medical implement in the lung of the subjectsubsequent to computing the displacement of the candidate medicalimplement.
 10. The method of claim 9 further comprising displaying ananimation of the medical implement as it is displaced.
 11. A medicalmethod for observing the efficacy of a region in the lung of a subjectcomprising the steps of: a) receiving first image data of the lung in afirst state; b) receiving second image data of the lung in a secondstate; c) registering the first image data and the second image datawith one another using a plurality of types of anatomical referencefeatures and wherein each of the anatomical reference features excludethe lung tissue and have minimal deflection between the first and secondstates; d) deforming at least one of the following: i) the first imagedata of the lung to match the second image data of the lung, and ii) thesecond image data of the lung to match the first image data of the lung;and e) generating a first motion vector field of the magnitude anddirection of local motion of lung tissue based on the deforming stepwherein the local motion of the lung tissue between first and secondstates is independent of the anatomical references defined in therigidly registering step.
 12. The method of claim 11 further comprising:receiving third image data of the lung during a third state; receivingfourth image data of the lung in a fourth state; registering the thirdimage data and the fourth image data with one another using ananatomical feature of the subject; deforming at least one of thefollowing: i) the third image data of the lung to match the fourth imagedata of the lung, and ii) the fourth image data of the lung to match thethird image data of the lung; and generating a second motion vectorfield of the magnitude and direction of local motion of lung tissuebased on the deforming step.
 13. The method of claim 12 furthercomprising automatically computing the difference between the firstmotion vector field and the second motion vector field.
 14. The methodof claim 13 wherein said second motion vector field is generatedfollowing a medical procedure performed on the subject.
 15. The methodof claim 14 wherein the medical procedure is a surgical procedure. 16.The method of claim 15 wherein the medical procedure is based onapplying radiation to a region of interest.
 17. The method of claim 14wherein the medical procedure is based on creating openings in theairway walls of the lung, the parenchyma, or removing a ROI.
 18. Themethod of claim 11 further comprising receiving a candidate region inthe lung.
 19. The method of claim 18 further comprising displaying thedisplacement of the candidate region based on the first motion vectorfield.
 20. The method of claim 19 further comprising placing a virtualfiducial marker in the lung and observing displacement of the virtualfiducial marker based on the first motion vector field.
 21. The methodof claim 11 wherein at least one of the plurality of reference featuresis selected from the group consisting of the spine and airway branches.22. The method of claim 11 wherein the first state corresponds to thelung during an inspiration by the subject.
 23. The method of claim 11wherein the first motion vector field corresponds to respiratory motionof the lung.
 24. The method of claim 11 further comprising sending to adisplay the first motion vector field.
 25. A medical analysis method forestimating a motion vector field of the magnitude and direction of localmotion of lung tissue of a subject comprising the steps of: a) receivingfirst image data of the lung in a first state; b) receiving second imagedata of the lung in a second state; c) identifying an anatomicalreference wherein the anatomical reference is a fixed anatomicalstructure and excludes lung tissue; d) registering the first image dataand the second image data with one another using the fixed anatomicalreference; d) deforming at least one of the following: i) the firstimage data of the lung to match the second image data of the lung, andii) the second image data of the lung to match the first image data ofthe lung; and e) estimating a motion vector field of the magnitude anddirection of local motion of lung tissue based on the deforming stepwherein the local motion of the lung tissue between first and secondstates is relative to an anatomical reference defined in the rigidlyregistering step.
 26. The method of claim 25 wherein the anatomicalreference is a spinal column.
 27. The method of claim 25 wherein thefirst state corresponds to the lung during an inspiration by thesubject.
 28. The method of claim 25 further comprising receiving a 3Dlocation of a candidate medical implement.
 29. The method of claim 28further comprising computing displacement of the candidate medicalimplement based on the motion vector field.
 30. The method of claim 29further comprising displaying the displacement of the medical implement.31. A medical analysis method for estimating a motion vector field ofthe magnitude and direction of local motion of lung tissue of a firstsubject comprising the steps of: a) receiving first image data of thelung of the first subject in a first state; b) generating an atlasmotion vector field, wherein the step of generating the atlas motionvector field comprises: receiving first image data of the lung of asecond subject in a first state; receiving second image data of the lungof the second subject in a second state; registering the first imagedata of the second subject and the second image data of the secondsubject with one another using a reference feature; deforming at leastone of the following: i) the first image data of the second subject tomatch the second image data of the second subject, and ii) the secondimage data of the second subject to match the first image data of thesecond subject; and estimating the atlas motion vector field based onthe deforming step; and c) estimating the local motion of the lung ofthe first subject based on the atlas motion vector field.
 32. The methodof claim 31 wherein the first image data of the first subject comprisesa set of 2D CT images of the lung.
 33. The method of claim 1 wherein thedeformation is performed by minimizing a cost function.
 34. The methodof claim 33 wherein minimizing the cost function is performed byminimizing a sum-of-squared-differences.